Overview

Dataset statistics

Number of variables35
Number of observations1033
Missing cells31
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory282.6 KiB
Average record size in memory280.1 B

Variable types

Categorical5
Boolean17
Numeric13

Alerts

desertion is highly correlated with ProgressHigh correlation
Student Visa is highly correlated with British and 1 other fieldsHigh correlation
British is highly correlated with Student VisaHigh correlation
Progress is highly correlated with desertionHigh correlation
Bursary is highly correlated with Polar 4 ScoreHigh correlation
A Levels is highly correlated with BtecHigh correlation
Btec is highly correlated with A LevelsHigh correlation
SLC is highly correlated with Student VisaHigh correlation
Polar 4 Score is highly correlated with BursaryHigh correlation
Course is highly correlated with UCASHigh correlation
UCAS is highly correlated with Course and 2 other fieldsHigh correlation
25 Above is highly correlated with UCASHigh correlation
Disability is highly correlated with BursaryHigh correlation
desertion is highly correlated with UCAS and 9 other fieldsHigh correlation
British is highly correlated with English native Language and 3 other fieldsHigh correlation
English native Language is highly correlated with BritishHigh correlation
SLC is highly correlated with British and 1 other fieldsHigh correlation
Care Leaver is highly correlated with RefugeeHigh correlation
Student Visa is highly correlated with British and 2 other fieldsHigh correlation
Refugee is highly correlated with Care LeaverHigh correlation
London Permanent Residence is highly correlated with British and 1 other fieldsHigh correlation
UCAS Points is highly correlated with EnglishHigh correlation
English is highly correlated with UCAS Points and 1 other fieldsHigh correlation
Maths is highly correlated with EnglishHigh correlation
A Levels is highly correlated with BtecHigh correlation
Btec is highly correlated with A LevelsHigh correlation
Bursary is highly correlated with DisabilityHigh correlation
Attendance is highly correlated with desertion and 2 other fieldsHigh correlation
AWM year 1 is highly correlated with desertion and 2 other fieldsHigh correlation
AWM year 2 is highly correlated with desertion and 3 other fieldsHigh correlation
AWM year 3 is highly correlated with desertion and 3 other fieldsHigh correlation
Overall AWM is highly correlated with desertion and 7 other fieldsHigh correlation
Progress is highly correlated with desertion and 7 other fieldsHigh correlation
First Sit is highly correlated with desertion and 2 other fieldsHigh correlation
Second Sit is highly correlated with First Sit and 1 other fieldsHigh correlation
Fails is highly correlated with desertion and 3 other fieldsHigh correlation
No Submissions is highly correlated with First Sit and 1 other fieldsHigh correlation
Pass is highly correlated with desertion and 3 other fieldsHigh correlation
Ethnicity has 13 (1.3%) missing values Missing
AWM year 2 has 112 (10.8%) zeros Zeros
AWM year 3 has 276 (26.7%) zeros Zeros
Second Sit has 208 (20.1%) zeros Zeros
Fails has 848 (82.1%) zeros Zeros
No Submissions has 423 (40.9%) zeros Zeros

Reproduction

Analysis started2022-08-10 10:26:17.785135
Analysis finished2022-08-10 10:26:54.923159
Duration37.14 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Course
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)1.1%
Missing2
Missing (%)0.2%
Memory size8.2 KiB
BA
395 
ba
380 
BA Business Management Enterpreneurship and Innovation
86 
BA Business Management
63 
Ba Business Management Finance
 
39
Other values (6)
68 

Length

Max length55
Median length2
Mean length10.39185257
Min length2

Characters and Unicode

Total characters10714
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowBA Business Manangement Enterpreneurship and Innovation
2nd rowBA Business Management
3rd rowBA Business Management Enterpreneurship and Innovation
4th rowBA Business Management
5th rowBA Business Management Enterpreneurship and Innovation

Common Values

ValueCountFrequency (%)
BA395
38.2%
ba380
36.8%
BA Business Management Enterpreneurship and Innovation86
 
8.3%
BA Business Management63
 
6.1%
Ba Business Management Finance39
 
3.8%
BA Business Management Marketing37
 
3.6%
BA Business Management International Business12
 
1.2%
MBA11
 
1.1%
Ba4
 
0.4%
BA Business Manangement Enterpreneurship and Innovation3
 
0.3%
(Missing)2
 
0.2%

Length

2022-08-10T11:26:55.046338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ba1020
54.2%
business253
 
13.5%
management238
 
12.7%
enterpreneurship89
 
4.7%
and89
 
4.7%
innovation89
 
4.7%
finance39
 
2.1%
marketing38
 
2.0%
international12
 
0.6%
mba11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n1424
13.3%
a1184
11.1%
e1091
10.2%
B904
 
8.4%
850
 
7.9%
s848
 
7.9%
A608
 
5.7%
i520
 
4.9%
t481
 
4.5%
b380
 
3.5%
Other values (18)2424
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7831
73.1%
Uppercase Letter2031
 
19.0%
Space Separator850
 
7.9%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1424
18.2%
a1184
15.1%
e1091
13.9%
s848
10.8%
i520
 
6.6%
t481
 
6.1%
b380
 
4.9%
u342
 
4.4%
r317
 
4.0%
g279
 
3.6%
Other values (9)965
12.3%
Uppercase Letter
ValueCountFrequency (%)
B904
44.5%
A608
29.9%
M290
 
14.3%
I101
 
5.0%
E89
 
4.4%
F39
 
1.9%
Space Separator
ValueCountFrequency (%)
850
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9862
92.0%
Common852
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1424
14.4%
a1184
12.0%
e1091
11.1%
B904
9.2%
s848
8.6%
A608
 
6.2%
i520
 
5.3%
t481
 
4.9%
b380
 
3.9%
u342
 
3.5%
Other values (15)2080
21.1%
Common
ValueCountFrequency (%)
850
99.8%
(1
 
0.1%
)1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1424
13.3%
a1184
11.1%
e1091
10.2%
B904
 
8.4%
850
 
7.9%
s848
 
7.9%
A608
 
5.7%
i520
 
4.9%
t481
 
4.5%
b380
 
3.5%
Other values (18)2424
22.6%

UCAS
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
938 
False
95 
ValueCountFrequency (%)
True938
90.8%
False95
 
9.2%
2022-08-10T11:26:55.220177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

25 Above
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
872 
True
161 
ValueCountFrequency (%)
False872
84.4%
True161
 
15.6%
2022-08-10T11:26:55.352524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Disability
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
967 
True
 
66
ValueCountFrequency (%)
False967
93.6%
True66
 
6.4%
2022-08-10T11:26:55.491176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Ethnicity
Categorical

MISSING

Distinct6
Distinct (%)0.6%
Missing13
Missing (%)1.3%
Memory size8.2 KiB
White
501 
Asian
279 
Black/Black British African
159 
Other ethnic background
76 
Other Black Background
 
3

Length

Max length27
Median length5
Mean length9.851960784
Min length5

Characters and Unicode

Total characters10049
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsian
2nd rowWhite
3rd rowAsian
4th rowWhite
5th rowAsian

Common Values

ValueCountFrequency (%)
White501
48.5%
Asian279
27.0%
Black/Black British African159
 
15.4%
Other ethnic background76
 
7.4%
Other Black Background3
 
0.3%
Mixed White and Asian2
 
0.2%
(Missing)13
 
1.3%

Length

2022-08-10T11:26:55.622137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-10T11:26:55.803161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
white503
33.5%
asian281
18.7%
black/black159
 
10.6%
british159
 
10.6%
african159
 
10.6%
other79
 
5.3%
background79
 
5.3%
ethnic76
 
5.1%
black3
 
0.2%
mixed2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i1339
13.3%
a842
 
8.4%
t817
 
8.1%
h817
 
8.1%
e660
 
6.6%
c635
 
6.3%
n597
 
5.9%
W503
 
5.0%
B483
 
4.8%
482
 
4.8%
Other values (15)2874
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7901
78.6%
Uppercase Letter1507
 
15.0%
Space Separator482
 
4.8%
Other Punctuation159
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1339
16.9%
a842
10.7%
t817
10.3%
h817
10.3%
e660
8.4%
c635
8.0%
n597
7.6%
r476
 
6.0%
s440
 
5.6%
k400
 
5.1%
Other values (8)878
11.1%
Uppercase Letter
ValueCountFrequency (%)
W503
33.4%
B483
32.1%
A440
29.2%
O79
 
5.2%
M2
 
0.1%
Space Separator
ValueCountFrequency (%)
482
100.0%
Other Punctuation
ValueCountFrequency (%)
/159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9408
93.6%
Common641
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1339
14.2%
a842
 
8.9%
t817
 
8.7%
h817
 
8.7%
e660
 
7.0%
c635
 
6.7%
n597
 
6.3%
W503
 
5.3%
B483
 
5.1%
r476
 
5.1%
Other values (13)2239
23.8%
Common
ValueCountFrequency (%)
482
75.2%
/159
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1339
13.3%
a842
 
8.4%
t817
 
8.1%
h817
 
8.1%
e660
 
6.6%
c635
 
6.3%
n597
 
5.9%
W503
 
5.0%
B483
 
4.8%
482
 
4.8%
Other values (15)2874
28.6%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
Male
639 
Female
394 

Length

Max length6
Median length4
Mean length4.762826718
Min length4

Characters and Unicode

Total characters4920
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male639
61.9%
Female394
38.1%

Length

2022-08-10T11:26:55.996132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-10T11:26:56.160485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
male639
61.9%
female394
38.1%

Most occurring characters

ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3887
79.0%
Uppercase Letter1033
 
21.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1427
36.7%
a1033
26.6%
l1033
26.6%
m394
 
10.1%
Uppercase Letter
ValueCountFrequency (%)
M639
61.9%
F394
38.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1427
29.0%
a1033
21.0%
l1033
21.0%
M639
13.0%
F394
 
8.0%
m394
 
8.0%

desertion
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
874 
True
159 
ValueCountFrequency (%)
False874
84.6%
True159
 
15.4%
2022-08-10T11:26:56.294157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

British
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
650 
False
383 
ValueCountFrequency (%)
True650
62.9%
False383
37.1%
2022-08-10T11:26:56.422371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

English native Language
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
566 
False
467 
ValueCountFrequency (%)
True566
54.8%
False467
45.2%
2022-08-10T11:26:56.555104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
572 
True
461 
ValueCountFrequency (%)
False572
55.4%
True461
44.6%
2022-08-10T11:26:56.688076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Polar 4 Score
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
3.0
454 
5.0
167 
4.0
156 
2.0
138 
1.0
118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3099
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0454
43.9%
5.0167
 
16.2%
4.0156
 
15.1%
2.0138
 
13.4%
1.0118
 
11.4%

Length

2022-08-10T11:26:56.810041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-10T11:26:56.968975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0454
43.9%
5.0167
 
16.2%
4.0156
 
15.1%
2.0138
 
13.4%
1.0118
 
11.4%

Most occurring characters

ValueCountFrequency (%)
.1033
33.3%
01033
33.3%
3454
14.6%
5167
 
5.4%
4156
 
5.0%
2138
 
4.5%
1118
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2066
66.7%
Other Punctuation1033
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01033
50.0%
3454
22.0%
5167
 
8.1%
4156
 
7.6%
2138
 
6.7%
1118
 
5.7%
Other Punctuation
ValueCountFrequency (%)
.1033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.1033
33.3%
01033
33.3%
3454
14.6%
5167
 
5.4%
4156
 
5.0%
2138
 
4.5%
1118
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.1033
33.3%
01033
33.3%
3454
14.6%
5167
 
5.4%
4156
 
5.0%
2138
 
4.5%
1118
 
3.8%

SLC
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
734 
False
299 
ValueCountFrequency (%)
True734
71.1%
False299
28.9%
2022-08-10T11:26:57.130914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Care Leaver
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
1014 
True
 
19
ValueCountFrequency (%)
False1014
98.2%
True19
 
1.8%
2022-08-10T11:26:57.271836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Student Visa
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
878 
True
155 
ValueCountFrequency (%)
False878
85.0%
True155
 
15.0%
2022-08-10T11:26:57.399752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Refugee
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
1009 
True
 
24
ValueCountFrequency (%)
False1009
97.7%
True24
 
2.3%
2022-08-10T11:26:57.525708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

London Permanent Residence
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
573 
False
460 
ValueCountFrequency (%)
True573
55.5%
False460
44.5%
2022-08-10T11:26:57.649676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

UCAS Points
Real number (ℝ≥0)

HIGH CORRELATION

Distinct61
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.113381
Minimum72
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:26:57.803058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile82
Q196
median106
Q3119
95-th percentile152
Maximum168
Range96
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.67005985
Coefficient of variation (CV)0.1802717473
Kurtosis1.053424327
Mean109.113381
Median Absolute Deviation (MAD)10
Skewness1.008062836
Sum112714.1226
Variance386.9112546
MonotonicityNot monotonic
2022-08-10T11:26:57.999087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9684
 
8.1%
109.11338154
 
5.2%
10451
 
4.9%
12847
 
4.5%
8036
 
3.5%
12036
 
3.5%
11235
 
3.4%
8435
 
3.4%
8833
 
3.2%
10033
 
3.2%
Other values (51)589
57.0%
ValueCountFrequency (%)
724
 
0.4%
8036
3.5%
8222
2.1%
8435
3.4%
851
 
0.1%
8610
 
1.0%
875
 
0.5%
8833
3.2%
897
 
0.7%
906
 
0.6%
ValueCountFrequency (%)
16825
2.4%
1625
 
0.5%
1608
 
0.8%
1551
 
0.1%
1538
 
0.8%
15215
1.5%
1486
 
0.6%
1464
 
0.4%
14418
1.7%
1366
 
0.6%

English
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.924398625
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:26:58.164092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4.924398625
Q35
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.273237614
Coefficient of variation (CV)0.2585569753
Kurtosis0.9778030284
Mean4.924398625
Median Absolute Deviation (MAD)0.9243986254
Skewness0.8164676538
Sum5086.90378
Variance1.621134021
MonotonicityNot monotonic
2022-08-10T11:26:58.294399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4290
28.1%
5256
24.8%
4.924398625160
15.5%
6120
11.6%
382
 
7.9%
853
 
5.1%
751
 
4.9%
211
 
1.1%
910
 
1.0%
ValueCountFrequency (%)
211
 
1.1%
382
 
7.9%
4290
28.1%
4.924398625160
15.5%
5256
24.8%
6120
11.6%
751
 
4.9%
853
 
5.1%
910
 
1.0%
ValueCountFrequency (%)
910
 
1.0%
853
 
5.1%
751
 
4.9%
6120
11.6%
5256
24.8%
4.924398625160
15.5%
4290
28.1%
382
 
7.9%
211
 
1.1%

Maths
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.774082569
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:26:58.438108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4.774082569
Q35
95-th percentile7
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.101661125
Coefficient of variation (CV)0.2307587079
Kurtosis1.45556824
Mean4.774082569
Median Absolute Deviation (MAD)0.7740825688
Skewness0.6200731769
Sum4931.627294
Variance1.213657235
MonotonicityNot monotonic
2022-08-10T11:26:58.570047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4345
33.4%
5256
24.8%
4.774082569161
15.6%
6124
 
12.0%
760
 
5.8%
346
 
4.5%
222
 
2.1%
814
 
1.4%
95
 
0.5%
ValueCountFrequency (%)
222
 
2.1%
346
 
4.5%
4345
33.4%
4.774082569161
15.6%
5256
24.8%
6124
 
12.0%
760
 
5.8%
814
 
1.4%
95
 
0.5%
ValueCountFrequency (%)
95
 
0.5%
814
 
1.4%
760
 
5.8%
6124
 
12.0%
5256
24.8%
4.774082569161
15.6%
4345
33.4%
346
 
4.5%
222
 
2.1%

A Levels
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
579 
False
454 
ValueCountFrequency (%)
True579
56.1%
False454
43.9%
2022-08-10T11:26:58.727038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Btec
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
654 
True
379 
ValueCountFrequency (%)
False654
63.3%
True379
36.7%
2022-08-10T11:26:58.856303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
541 
False
492 
ValueCountFrequency (%)
True541
52.4%
False492
47.6%
2022-08-10T11:26:59.002326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Bursary
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
788 
True
245 
ValueCountFrequency (%)
False788
76.3%
True245
 
23.7%
2022-08-10T11:26:59.149150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Attendance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)6.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean75.07751938
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:26:59.317617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile46
Q164
median76
Q388
95-th percentile97
Maximum100
Range80
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.7430199
Coefficient of variation (CV)0.2096901979
Kurtosis-0.6289405519
Mean75.07751938
Median Absolute Deviation (MAD)12
Skewness-0.395967175
Sum77480
Variance247.8426755
MonotonicityNot monotonic
2022-08-10T11:26:59.533054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6034
 
3.3%
9231
 
3.0%
9529
 
2.8%
7428
 
2.7%
9627
 
2.6%
8127
 
2.6%
9027
 
2.6%
7226
 
2.5%
8825
 
2.4%
6525
 
2.4%
Other values (53)753
72.9%
ValueCountFrequency (%)
201
 
0.1%
251
 
0.1%
406
0.6%
416
0.6%
4214
1.4%
433
 
0.3%
448
0.8%
4512
1.2%
467
0.7%
4710
1.0%
ValueCountFrequency (%)
10015
1.5%
9915
1.5%
9820
1.9%
9715
1.5%
9627
2.6%
9529
2.8%
9425
2.4%
9313
1.3%
9231
3.0%
9116
1.5%

AWM year 1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)5.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.71414729
Minimum30
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:26:59.748013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q146
median58
Q371
95-th percentile82
Maximum85
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.24820418
Coefficient of variation (CV)0.2426707164
Kurtosis-1.117789841
Mean58.71414729
Median Absolute Deviation (MAD)12
Skewness0.1441683938
Sum60593
Variance203.0113225
MonotonicityNot monotonic
2022-08-10T11:26:59.937053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4138
 
3.7%
4434
 
3.3%
4334
 
3.3%
4534
 
3.3%
4731
 
3.0%
4629
 
2.8%
8029
 
2.8%
4228
 
2.7%
4027
 
2.6%
5026
 
2.5%
Other values (46)722
69.9%
ValueCountFrequency (%)
305
0.5%
314
0.4%
325
0.5%
335
0.5%
344
0.4%
355
0.5%
366
0.6%
372
 
0.2%
386
0.6%
396
0.6%
ValueCountFrequency (%)
8519
1.8%
8417
1.6%
8312
1.2%
8213
1.3%
818
 
0.8%
8029
2.8%
7917
1.6%
7813
1.3%
7724
2.3%
7620
1.9%

AWM year 2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.93320426
Minimum0
Maximum87
Zeros112
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:00.155082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143
median58
Q372
95-th percentile83
Maximum87
Range87
Interquartile range (IQR)29

Descriptive statistics

Standard deviation23.69943395
Coefficient of variation (CV)0.4394219531
Kurtosis0.3619090467
Mean53.93320426
Median Absolute Deviation (MAD)15
Skewness-0.9754745629
Sum55713
Variance561.6631697
MonotonicityNot monotonic
2022-08-10T11:27:00.340079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0112
 
10.8%
7927
 
2.6%
4027
 
2.6%
5525
 
2.4%
7125
 
2.4%
4923
 
2.2%
8123
 
2.2%
6222
 
2.1%
4622
 
2.1%
8322
 
2.1%
Other values (48)705
68.2%
ValueCountFrequency (%)
0112
10.8%
303
 
0.3%
316
 
0.6%
3211
 
1.1%
339
 
0.9%
346
 
0.6%
3514
 
1.4%
367
 
0.7%
375
 
0.5%
3812
 
1.2%
ValueCountFrequency (%)
8712
1.2%
8515
1.5%
8414
1.4%
8322
2.1%
8218
1.7%
8123
2.2%
8016
1.5%
7927
2.6%
7819
1.8%
7718
1.7%

AWM year 3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.23233301
Minimum0
Maximum85
Zeros276
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:00.540040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median49
Q367
95-th percentile82
Maximum85
Range85
Interquartile range (IQR)67

Descriptive statistics

Standard deviation29.01446846
Coefficient of variation (CV)0.6711289083
Kurtosis-1.178751671
Mean43.23233301
Median Absolute Deviation (MAD)19
Skewness-0.4536500729
Sum44659
Variance841.8393799
MonotonicityNot monotonic
2022-08-10T11:27:00.723015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0276
26.7%
4325
 
2.4%
4124
 
2.3%
4423
 
2.2%
5222
 
2.1%
6320
 
1.9%
4520
 
1.9%
6519
 
1.8%
7619
 
1.8%
6619
 
1.8%
Other values (47)566
54.8%
ValueCountFrequency (%)
0276
26.7%
302
 
0.2%
316
 
0.6%
322
 
0.2%
335
 
0.5%
345
 
0.5%
357
 
0.7%
3610
 
1.0%
3712
 
1.2%
384
 
0.4%
ValueCountFrequency (%)
8513
1.3%
8415
1.5%
8310
1.0%
8217
1.6%
816
 
0.6%
8017
1.6%
7913
1.3%
7812
1.2%
7712
1.2%
7619
1.8%

Overall AWM
Real number (ℝ≥0)

HIGH CORRELATION

Distinct167
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.06034205
Minimum0
Maximum84
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:00.921962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q151
median59.33333333
Q366.33333333
95-th percentile74.66666667
Maximum84
Range84
Interquartile range (IQR)15.33333333

Descriptive statistics

Standard deviation11.39589884
Coefficient of variation (CV)0.196276812
Kurtosis0.221483954
Mean58.06034205
Median Absolute Deviation (MAD)7.333333333
Skewness-0.5206733458
Sum59976.33333
Variance129.8665103
MonotonicityNot monotonic
2022-08-10T11:27:01.114939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.3333333324
 
2.3%
58.3333333319
 
1.8%
67.3333333317
 
1.6%
6017
 
1.6%
6716
 
1.5%
6416
 
1.5%
60.6666666716
 
1.5%
5715
 
1.5%
6115
 
1.5%
56.3333333315
 
1.5%
Other values (157)863
83.5%
ValueCountFrequency (%)
01
 
0.1%
20.51
 
0.1%
27.333333331
 
0.1%
27.666666671
 
0.1%
305
0.5%
314
0.4%
325
0.5%
335
0.5%
344
0.4%
356
0.6%
ValueCountFrequency (%)
841
 
0.1%
82.666666671
 
0.1%
822
0.2%
812
0.2%
80.666666671
 
0.1%
80.51
 
0.1%
80.333333332
0.2%
803
0.3%
793
0.3%
78.51
 
0.1%

Progress
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size2.1 KiB
True
848 
False
184 
(Missing)
 
1
ValueCountFrequency (%)
True848
82.1%
False184
 
17.8%
(Missing)1
 
0.1%
2022-08-10T11:27:01.359276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

First Sit
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.016472868
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:01.472080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.303847906
Coefficient of variation (CV)0.3246250998
Kurtosis-0.7057159947
Mean4.016472868
Median Absolute Deviation (MAD)1
Skewness0.02471814333
Sum4145
Variance1.700019361
MonotonicityNot monotonic
2022-08-10T11:27:01.608015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3373
36.1%
4218
21.1%
6189
18.3%
5183
17.7%
236
 
3.5%
133
 
3.2%
(Missing)1
 
0.1%
ValueCountFrequency (%)
133
 
3.2%
236
 
3.5%
3373
36.1%
4218
21.1%
5183
17.7%
6189
18.3%
ValueCountFrequency (%)
6189
18.3%
5183
17.7%
4218
21.1%
3373
36.1%
236
 
3.5%
133
 
3.2%

Second Sit
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing8
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.834146341
Minimum0
Maximum5
Zeros208
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:01.739969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.251485398
Coefficient of variation (CV)0.6823258154
Kurtosis-0.8062111839
Mean1.834146341
Median Absolute Deviation (MAD)1
Skewness-0.01364452657
Sum1880
Variance1.566215701
MonotonicityNot monotonic
2022-08-10T11:27:01.870940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3349
33.8%
2231
22.4%
0208
20.1%
1199
19.3%
520
 
1.9%
418
 
1.7%
(Missing)8
 
0.8%
ValueCountFrequency (%)
0208
20.1%
1199
19.3%
2231
22.4%
3349
33.8%
418
 
1.7%
520
 
1.9%
ValueCountFrequency (%)
520
 
1.9%
418
 
1.7%
3349
33.8%
2231
22.4%
1199
19.3%
0208
20.1%

Fails
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.5639534884
Minimum0
Maximum5
Zeros848
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:02.006883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.309437159
Coefficient of variation (CV)2.321888571
Kurtosis3.595023888
Mean0.5639534884
Median Absolute Deviation (MAD)0
Skewness2.20925508
Sum582
Variance1.714625674
MonotonicityNot monotonic
2022-08-10T11:27:02.144871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0848
82.1%
253
 
5.1%
350
 
4.8%
439
 
3.8%
532
 
3.1%
110
 
1.0%
(Missing)1
 
0.1%
ValueCountFrequency (%)
0848
82.1%
110
 
1.0%
253
 
5.1%
350
 
4.8%
439
 
3.8%
532
 
3.1%
ValueCountFrequency (%)
532
 
3.1%
439
 
3.8%
350
 
4.8%
253
 
5.1%
110
 
1.0%
0848
82.1%

No Submissions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.234496124
Minimum0
Maximum5
Zeros423
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:02.274808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.364384136
Coefficient of variation (CV)1.105215407
Kurtosis-0.06991856543
Mean1.234496124
Median Absolute Deviation (MAD)1
Skewness0.9483452924
Sum1274
Variance1.861544072
MonotonicityNot monotonic
2022-08-10T11:27:02.405767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0423
40.9%
1252
24.4%
2165
 
16.0%
396
 
9.3%
476
 
7.4%
520
 
1.9%
(Missing)1
 
0.1%
ValueCountFrequency (%)
0423
40.9%
1252
24.4%
2165
 
16.0%
396
 
9.3%
476
 
7.4%
520
 
1.9%
ValueCountFrequency (%)
520
 
1.9%
476
 
7.4%
396
 
9.3%
2165
 
16.0%
1252
24.4%
0423
40.9%

Late Submission
Categorical

Distinct4
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size8.2 KiB
1.0
423 
0.0
409 
2.0
175 
3.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3096
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0423
40.9%
0.0409
39.6%
2.0175
16.9%
3.025
 
2.4%
(Missing)1
 
0.1%

Length

2022-08-10T11:27:02.551710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-10T11:27:02.704614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0423
41.0%
0.0409
39.6%
2.0175
17.0%
3.025
 
2.4%

Most occurring characters

ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2064
66.7%
Other Punctuation1032
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01441
69.8%
1423
 
20.5%
2175
 
8.5%
325
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.1032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3096
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01441
46.5%
.1032
33.3%
1423
 
13.7%
2175
 
5.7%
325
 
0.8%

Pass
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean91.62080103
Minimum16.66666667
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-08-10T11:27:02.841404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.66666667
5-th percentile33.33333333
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range83.33333333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.83237346
Coefficient of variation (CV)0.216461472
Kurtosis3.835663087
Mean91.62080103
Median Absolute Deviation (MAD)0
Skewness-2.272267351
Sum94552.66667
Variance393.3230371
MonotonicityNot monotonic
2022-08-10T11:27:02.966071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
100848
82.1%
33.3333333352
 
5.0%
5050
 
4.8%
66.6666666739
 
3.8%
83.3333333332
 
3.1%
16.6666666710
 
1.0%
861
 
0.1%
(Missing)1
 
0.1%
ValueCountFrequency (%)
16.6666666710
 
1.0%
33.3333333352
 
5.0%
5050
 
4.8%
66.6666666739
 
3.8%
83.3333333332
 
3.1%
861
 
0.1%
100848
82.1%
ValueCountFrequency (%)
100848
82.1%
861
 
0.1%
83.3333333332
 
3.1%
66.6666666739
 
3.8%
5050
 
4.8%
33.3333333352
 
5.0%
16.6666666710
 
1.0%

Interactions

2022-08-10T11:26:50.164493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:24.315012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:26.405878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:28.567431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:30.776385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:33.368414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:35.469264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:37.626019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:39.693930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:41.792198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:43.892216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.999356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:48.051136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:50.326086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:24.473984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:26.575424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:28.725374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:30.956017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:33.523072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:35.622209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:37.783371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:39.843831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:41.959250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:44.050304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:46.153094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:48.212106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:50.492458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:24.641884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:26.752949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:28.930329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:31.132437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:33.696938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:35.789152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:37.945039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.014759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:42.126050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:44.213866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:46.315038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:48.380074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:50.655547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:24.800275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:26.923493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:29.105226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:31.310054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:33.867392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:35.955109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:38.112033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.181715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:42.297508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:44.386405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:46.483485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:48.556987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:50.831095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:24.980994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:27.100022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:29.282621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:31.495326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.038862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:36.126073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:38.288315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.362647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:42.482389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:44.578891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:46.653030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:48.735944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:50.987536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:25.137930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:27.257600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:29.450172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:31.659535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.194315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:36.295037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:38.462087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.529090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:42.640965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:44.737468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:46.811125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:48.898943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:51.132146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:25.304857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:27.413183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:29.609009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:31.824093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.344913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:36.444443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:38.612048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.674000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:42.790565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:44.889610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:46.960725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:49.057355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:51.299079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:25.478287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:27.582277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:29.769922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:32.270085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.494512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:36.592113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:38.762020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.822455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:42.938607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.042252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:47.108329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:49.208507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:51.461482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:25.628430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:27.741365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:29.943359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:32.489020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.657079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:36.737524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:38.907433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:40.979319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:43.102170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.211485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:47.267165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:49.372069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:51.614532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:25.779027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:27.903453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:30.104975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:32.678012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.813659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:36.893955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:39.061835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:41.142033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:43.255759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.371193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:47.423594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:49.539988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:51.768165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:25.936606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:28.063025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:30.276927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:32.844948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:34.991346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:37.043868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:39.215729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:41.307480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:43.411342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.535129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:47.587707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:49.697944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:51.917542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:26.089708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:28.229579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:30.442344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:33.029910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:35.145271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:37.305835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:39.368692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:41.468898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:43.575902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.691091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:47.742606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:49.855858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:52.069004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:26.246304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:28.408510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:30.615005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:33.201085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:35.314512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:37.477251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:39.538015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:41.629804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:43.741282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:45.849059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:47.899187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-10T11:26:50.012151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-10T11:27:03.131628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-10T11:27:03.600390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-10T11:26:52.397083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-10T11:26:53.890985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-10T11:26:54.325070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-10T11:26:54.649632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CourseUCAS25 AboveDisabilityEthnicityGenderdesertionBritishEnglish native LanguageParent He attendancePolar 4 ScoreSLCCare LeaverStudent VisaRefugeeLondon Permanent ResidenceUCAS PointsEnglishMathsA LevelsBtecPrevious workBursaryAttendanceAWM year 1AWM year 2AWM year 3Overall AWMProgressFirst SitSecond SitFailsNo SubmissionsLate SubmissionPass
0BA Business Manangement Enterpreneurship and InnovationnononoAsianMalenononoyes3.0nonoyesnoyes98.05.04.0yesnoyesno86.085.058.043.062.0yes3.03.00.02.02.0100.0
1BA Business ManagementnononoWhiteMaleyesnonoyes2.0yesnononono101.05.05.0yesnoyesyes55.040.032.00.036.0no1.02.05.03.00.083.333333
2BA Business Management Enterpreneurship and InnovationnononoAsianMaleyesyesyesyes3.0yesnononoyes129.04.04.0yesnoyesno57.041.00.00.041.0yes6.00.00.00.00.0100.0
3BA Business ManagementnoyesnoWhiteFemaleyesnonono3.0yesnononoyes110.09.08.0yesnoyesno48.041.043.00.042.0yes6.00.00.00.00.0100.0
4BA Business Management Enterpreneurship and InnovationnononoAsianMalenoyesyesyes3.0yesnononoyes130.06.05.0yesnoyesno83.055.049.059.054.333333yes4.02.00.02.00.0100.0
5BA Business Management Enterpreneurship and InnovationyesnonoAsianMalenoyesyesyes3.0yesnononoyes112.06.04.0noyesnono71.046.046.043.045.0yes3.03.00.00.01.0100.0
6BA Business Management MarketingyesnonoWhiteMalenonoyesno5.0nonoyesnono89.06.05.0yesnonono96.078.070.079.075.666667yes4.02.00.00.02.0100.0
7BA Business Management Enterpreneurship and InnovationyesnonoWhiteMalenoyesyesno3.0yesnononoyes103.04.05.0yesnonono67.043.085.061.063.0yes3.03.00.03.00.0100.0
8BA Business Management Enterpreneurship and InnovationyesnonoWhiteMalenoyesyesno2.0nonononoyes128.04.04.0noyesnoyes89.076.058.044.059.333333yes6.00.00.00.00.0100.0
9BA Business ManagementyesnonoWhiteFemalenoyesyesno2.0nonononono91.04.04.0nononono92.049.083.067.066.333333yes6.00.00.01.01.0100.0

Last rows

CourseUCAS25 AboveDisabilityEthnicityGenderdesertionBritishEnglish native LanguageParent He attendancePolar 4 ScoreSLCCare LeaverStudent VisaRefugeeLondon Permanent ResidenceUCAS PointsEnglishMathsA LevelsBtecPrevious workBursaryAttendanceAWM year 1AWM year 2AWM year 3Overall AWMProgressFirst SitSecond SitFailsNo SubmissionsLate SubmissionPass
1023BAyesnonoAsianMalenononoyes5.0yesnononono107.06.07.0noyesnono96.080.00.00.080.0yes6.00.00.00.01.0100.0
1024BAyesyesnoWhiteMalenoyesyesno3.0yesnononono103.05.06.0noyesyesno67.040.00.00.040.0yes1.05.00.03.00.0100.0
1025BAyesnonoNaNMalenoyesyesyes3.0yesnononono100.05.04.0yesnoyesno70.053.00.00.053.0yes6.00.00.00.01.0100.0
1026BAyesyesnoNaNFemalenononoyes3.0yesnononono113.03.06.0noyesnono64.073.00.00.073.0yes3.03.00.02.01.0100.0
1027BAyesyesnoNaNMalenoyesnoyes2.0yesnononoyes118.05.05.0yesnoyesyes96.068.00.00.068.0yes3.03.00.01.00.0100.0
1028BAyesnonoOther ethnic backgroundFemaleyesnoyesno2.0yesnononono102.04.04.0yesnoyesno55.045.00.00.045.0yes6.00.00.00.01.0100.0
1029BAyesnonoNaNMalenoyesyesyes4.0yesnononoyes109.04.04.0yesnoyesno66.077.00.00.077.0yes6.00.00.00.00.0100.0
1030BAnoyesnoAsianFemaleyesnonono1.0nonononono104.06.05.0yesnoyesno42.033.00.00.033.0no1.01.02.04.01.033.333333
1031BAnoyesnoOther ethnic backgroundMalenononoyes4.0yesnononono101.06.06.0noyesnono60.076.00.00.076.0yes6.00.00.00.00.0100.0
1032BAnoyesnoOther ethnic backgroundFemalenononono3.0nonoyesnono104.08.04.0nononono71.080.00.00.080.0yes6.00.00.00.00.0100.0